Implementation of the Methyl-Seq

platform to identify targets of stress

and glucocorticoid exposure in

rodents and humans.

Richard S. Lee, Ph.D.

The Johns Hopkins Mood Disorders Center

“Agilent products are For Research Use Only. Not for use in diagnostic procedures”

Table of Contents

• Stress and its role in psychiatric illnesses • Glucocorticoid signaling • Epigenetics and tools • FKBP5 as a candidate gene for epigenetic study • Justification for Methyl-Seq • Mouse Methyl-Seq and glucocorticoids • Rat Methyl-Seq and Stress

– Design – Validation – Chronic stress exposure

• Human Methyl-Seq and Stress • Current and Future Directions using the Methyl-Seq platform

The Stress Response

NE

NE

NE

NE

NE

NE

ACTH

CRH

heart rate

blood pressure

GI function

reproductive function

immune function

inflammation

energy mobilization

energy storage

growth

epinephrine release

glucocorticoid release

(cortisol or corticosterone)

respiration

ACh

NE

McEwen et al., 2015

Chronic Stress and Disease

NE

NE

NE

NE

NE

NE

ACh

NE

Cardiovascular disease

Hypertension

Irritable bowel syndrome

Diarrhea

Cancer metastasis

Obesity

Diabetes

Loss of libido

Sexual dysfunction

Amenorrhea

Impaired fertility

Infection susceptibility

Depression

Anxiety disorders

Post-traumatic stress disorder

ACTH

CRH

McEwen et al., 2015

Stress and stress hormone

• Stress exposure is a robust risk factor for anxiety and mood disorders, as well as neurological diseases such as AD

• Dysregulation of stress response intimately tied to mental illnesses

• Stress hormone (and response) can be measured in plasma

Role of glucocorticoids in psychiatry

• Hypercortisolemia and glucocorticoid resistance often comorbid with depression

• Glucocorticoids are one of the few molecules that can trigger mania and depression in BP

• Cushing’s Disease – 60~90% of Cushing’s patients develop depression

• Depressive symptoms alleviated with resolution of hypercortisolemia

• Stress in the 21st century – Black Death

Fardet et al., 2012 American Journal of Psychiatry

“HPA-axis genes”

Lee/Sawa

Neuroendocrinology 2014;100:278–287DOI: 10.1159/000369585

280

dexamethasone suppression test. Remarkably, depression is comorbid in 60–90% of these Cushing’s patients [28–30] , and resolution of both hypercortisolemia and depressive symptoms by surgical removal of the adenomas suggests a causal role for hypercortisolemia and HPA axis dysregula-tion in the mood of these patients [29, 31] . Similarly, a landmark epidemiological study that examined hundreds of thousands of patients who were prescribed glucocorti-coids (i.e., iatrogenic Cushing’s syndrome) for nonpsychi-atric disorders found a significant increase in cases of de-pression, suicide, mania, and anxiety associated with glu-cocorticoid therapy [32] . Taken together, these studies highlight the importance of stress and its principal in vivo agent cortisol in mood regulation and necessitate a closer examination of the processes that govern this relationship.

Epigenetics and the HPA Axis: Case Studies

The following studies using animal models and epi-genetic tools have reported associations between behav-ioral changes relevant to mental illness and genes that are

either targets of or directly regulate HPA axis function (HPA axis genes; table 1 ).

Influences of Poor Maternal Care on Neurodevelopment Stress-mediated epigenetic modifications may be

more pronounced during the stress-vulnerable, early-life period where regions implicated in emotionality and stress reactivity such as the hippocampus, amygdala, and the prefrontal cortex are undergoing rapid changes in dendritic density, myelination, and synaptic plasticity [33, 34] . Weaver et al. [35] have reported that good ma-ternal nursing behavior (vs. neglect) is required for prop-er postnatal epigenetic programming of HPA axis func-tion in adulthood. They examined exon I 7 promoter re-gion of Nr3c1 (glucocorticoid receptor, Gr ) and found that CpG dinucleotides that reside within the binding sites for the nerve growth factor-inducible protein NGFI-A are heavily methylated in the pups that experienced poor nursing behavior. This differential methylation state was associated with decreased binding to NGFI-A, de-creased Nr3c1 expression, elevated plasma corticosterone

Table 1. Representative stress-/HPA axis-associated genes

Location Symbol Name Function Significance

Extracellularand cell surface

CRH (or CRF) Corticotropin-releasing hormone CRH signaling Trauma, depression, suicide, and animal models of stress [77 – 80]CRHR1 CRH receptor, Type 1 CRH signaling

CRHR2 CRH receptor, Type 2 CRH signaling

CRHBP CRH binding protein CRH signaling

Cytoplasmic FKBP4 FK506 binding protein 4 GC sensitivity Animal model of stress [39, 81]

FKBP5 FK506 binding protein 5 GC sensitivity Depression, PTSD, suicide, bipolar disorder [40, 41, 43, 82]

HSP70/HSPA1 Heat-shock protein, 70 kDa, 1A,1B, and 1L paralogs

GC sensitivity Stress and schizophrenia [83 – 86]

HSP90AA1 Heat-shock protein, 90 kDa GC sensitivity

GR Glucocorticoid receptor GC sensitivity Suicide and depression [87 – 89]

Nuclear BAG1 BCL2-associated athanogene 1 GC sensitivity Manic and depressive symptoms in mice [90, 91]

BDNF Brain-derived neurotrophic factor Target gene Anxiety and depressive phenotypes [64, 92, 93]

PER1 Period homolog 1 Target gene Circadian rhythm in bipolar disorder [94 – 96]

SGK1 Serum/GC-regulated kinase 1 Target gene Animal model of stress [97, 98]

TH Tyrosine hydroxylase Target gene Neurotransmission and psychosis [49, 99 – 101]

GC = Glucocorticoid.

Dow

nlo

aded

by:

JH

U J

ohn

Hopkin

s U

niv

ers

ity

162.1

29.2

51.6

9 -

5/1

9/2

016

9:3

4:0

6 P

M

Lee and Sawa, 2014

Homeostatic Cortisol

Dynamics

HPA-axis genes

Stress or Glucocorticoids

Disease: e.g. BDNF and TH

Epigenetic alterations: DNA methylation and Histone modifications

Changes in HPA-axis function: e.g. CRH, FKBP5, and GR

HPA-axis

Stress and glucocorticoid epigenetics

Lee and Sawa, 2014

Mechanism of action of cortisol

Wochnik et al, 2004

What is epigenetics?

• Study of heritable changes in gene function or phenotype, caused by mechanisms other than changes in underlying DNA sequence

• Study and measurement of transcriptional context, potential, and inertia

Tools of epigenetics

• Detection of DNA methylation

• Detection of Histone modifications

• Detection of gene-modifying RNA species

• Candidate assays and genome-wide approaches using arrays or next-generation sequencing

Bisulfite conversion of DNA

ACCTCGAGTTACCGTA ATTTCGAGTTATTGTA

Determination of % Methylation

50% 75% 25% 0%

45.7% 79.2% 25.5% 4.2%

.

.

.

X109~12?

Colony sequencing Pyrosequencing

Pyrosequencing

Biotage, 2005

FKBP5 in literature

• SNPs in FKBP5 associated with increased recurrence of depressive

episodes and response to antidepressant treatment (Binder et al.,

2004)

• Family-based association of FKBP5 to BP (Willour et al., 2009).

• Interaction of FKBP5 with childhood adversity on risk for PTSD ( Xie

et al., 2010)

• DNA sequence variants of the FKBP5 gene are associated with

unipolar depression (Zobel et al., 2010)

• Interaction of FKBP5, a stress-related gene, with childhood trauma

increases the risk for attempting suicide (Roy et al., 2010)

• SNPs in FKBP5 modulate the Trier response (Mahon and Wand,

2014)

FKBP5

FKBP5

FKBP5 FKBP5

FKBP5 FKBP5

Mechanism of action of FKBP5

X

Genomic organization of Fkbp5

CpG islands

118 kb

Ex11 AltEx1

Chr17: 28,536,042 bp

Ex1

28,654,469 bp

Intron 1 Intron 5 Promoter

a.

40

50

60

70

80

90

0 2 4 6 8

% D

NA

m

Weeks treated with CORT CpG-1

40

50

60

70

80

90

0 2 4 6 8

% D

NA

m

Weeks treated with CORT CpG-2

0

0.5

1

1.5

2

2.5

3

0 2 4 6 8

Rea

l-ti

me

exp

ress

ion

Weeks treated with CORT

b. c. d.

Control CORT

* *

* * *

* * * * *

* *

*

*

CORT CORT CORT RECOVERY RECOVERY RECOVERY

50

60

70

80

90

100

CpG-1 CpG-2

% D

NA

m

Hippocampus: Fkbp5 intron 1

0

10

20

30

40

50

60

70

CpG-1 CpG-2 CpG-3 CpG-4

% D

NA

m

Hippocampus: Fkbp5 intron 5

0

10

20

30

40

50

60

70

CpG-1 CpG-2 CpG-3 CpG-4

% D

NA

m

Hypothalamus: Fkbp5 intron 5

a. b.

c. d.

*

* *

*

*

* *

* *

* *

50

60

70

80

90

100

CpG-1 CpG-2

% D

NA

m

Hypothalamus: Fkbp5 intron 1

*

DNA methylation patterns in the brain

Control CORT Control Recovery CORT Recovery

60

65

70

75

80

85

90

95

100

0 2 4 6 8 10 12 14

% D

NA

m

Days treated with CORT: HT22 Fkbp5 intron 1 CpG-2

c.

60

65

70

75

80

85

90

95

100

0 2 4 6 8 10 12 14

% D

NA

m

Days treated with CORT: HT22 Fkbp5 intron 5 CpG-3

b.

0

0.5

1

1.5

2

2.5

3

3.5

4

0 2 4 6 8 10 12 14

Rel

ativ

e re

al-t

ime

exp

ress

ion

Days treated with CORT: HT22 Fkbp5

a.

CORT RECOVERY

CORT RECOVERY

* * *

* *

*

* *

*

*

*

CORT RECOVERY

Fkbp5 in the cell line

Control

CORT

DNA methylation vs. Plasma CORT levels

A P = 2.3 x 10-9

r² = 0.69

20

40

60

80

100

0 200 400 600 800

% D

NA

m a

t P

os

.1

Plasma CORT (ng/mL)

P = 0.00036 r² = 0.34

0

1

2

3

4

5

6

0 200 400 600 800

Rela

tive e

xp

res

sio

n o

f F

kb

p5

Plasma CORT (ng/mL)

P = 2.8 x 10-5 r² = 0.56

0.0

2.0

4.0

6.0

8.0

20 40 60 80 100

Sp

lee

n w

eig

ht

(mg

/g B

W)

% DNAm at Pos.1

P = 9.0 x 10-5 r² = 0.51

5

10

15

20

25

30

20 40 60 80 100

% v

isc

era

l fa

t

% DNAm at Pos.1

C D

B

Blood DNA methylation vs. brain DNA methylation and expression of Fkbp5

a) b)

30

40

50

60

70

80

90

20 30 40 50% D

NA

Meth

yla

tio

n in

Blo

od

% DNA Methylation in Hippocampus

Blood Intron 1 CpG-2 DNAm vs. Hippocampal Intron 5 CpG-4 DNAm

30

40

50

60

70

80

90

0.8 1 1.2 1.4 1.6 1.8% D

NA

Meth

yla

tio

n in

Blo

od

Relative Expression in Hippocampus (a.u.)

Blood Intron 1 CpG-2 DNAm vs. Hippocampal mRNA

R2 = 0.43

P = 3.5x10-5

0.8

1

1.2

1.4

1.6

1.8

2

20 25 30 35 40 45 50

Rela

tive e

xp

ressio

n (

a.u

.)

% DNA Methylation in Hippocampus

Hippocampal mRNA vs. Hippocampal Intron 5 CpG-4 DNAm

c)

R2 = 0.49

P = 2.7x10-5

R2 = 0.29

P = 0.003

Genome-wide investigation

• What other targets are epigenetically altered by glucocorticoids?

• What can a genome-wide assay tell us about glucocorticoid biology?

Sequence Capture Platform (Methyl-Seq) followed by bisulfite conversion and next generation sequencing.

Table 1. Mouse Methylome Target Capture Design. There was approximately 10 Mb of target overlap between data repositories resulting in a final hybridization capture array targeting 99 Mb across the murine genome. TFBS; transcription factor binding sites.

Hing et al., 2015

Analysis step Quality control Alignment Duplicate removal Methylation calling

Data type Raw paired-end sequences Quality-controlled sequences Quality-trimmed sequences Adapter-trimmed Sequences

Output Per sequence base quality Per tile sequence quality Per sequence quality scores Per base sequence content Per sequence GC content Per base N content Sequence length distribution Sequence duplication levels Overrepresented sequences Adapter and kmer content Uniquely mapped paired-end aligned sequences Deduplicated uniquely mapped paired-end aligned sequences Methylation calls

Optional Steps Alignment quality control

Tools FASTQC v0.11.3 Trim Galore v0.3.7 Bismark v0.13.0 Bowtie 2 v2.1.0 Samtools v0.1.19 BamUtil v1.0.12

HIPPOCAMPUS

VEHICLE VEHICLE CORT CORT

Sample_HPC1 Sample_HPC3 Sample_HPC2 Sample_HPC4

Total paired end (pe) reads 42,346,826 41,741,546 40,059,427 35,260,700

Total uniquely mapped pe reads 29,107,648 32,733,678 31,775,425 26,470,496

Mapping efficiency (Total uniquely mapped pe

reads / Total pe reads) 68.70 78.40 79.30 75.10

Duplicate reads (% of Total uniquely mapped

reads) 27.51 10.68 12.56 21.91

Total deduplicated uniquely mapped pe reads 21,101,491 29,237,995 27,784,971 20,670,502

CpGs (1.45M) coverage with at least 10 reads 48x 39x 40x 39x

“On Target”

Total uniquely pe reads 18,498,282 25,755,305 23,909,147 18,075,210

% of Deduplicated total uniquely mapped

reads 87.66 88.09 86.05 87.44

Average read depth 26x 35x 31x 24x

Number of bases mapped 147Mb 149Mb 152Mb 148Mb

Percent of mapped target bases covered by:

at least 10 reads 55.11 66.04 62.8 57.55

DMR#97: Snx18

b)

a)

Mouse MethylSeq DMR List Output and Bsmooth Plot

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chr13: 113,613,597 − 113,614,253 (width = 657, extended = 5,000)

Smoothing provides corrected methylation percentage estimates based on Correlation between CpGs and also provdies T-stat

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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)M

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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)

0

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VEHICLE

CORT

** **

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Hippocampus: EfnB2

Chr8: 8,621,381-8,622,128

a) b)

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chr13: 52,975,989 − 52,977,245 (width = 1,257, extended = 5,000)

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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)

Genome-wide DNA methylation:

Mouse MethylSeq Results and Pyrosequencing of DMRs in HPC and Blood

Blood: Nfil3

c)

Chr13: 52,975,989-52,977,245

0

20

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60

80

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5

VEHICLE

CORT

d)

*** ***

*** ***

***

**p<0.01

***p<0.001

10.1%

12.8%

54.7%

35.6%

Blood DMRs

8.9%

14.9%

52.5%

36.8%

Hippocampal DMRs

Promoter

Exon

Intron

Intergenic

0.8%

22.1%

77.6%

Blood DMRs

1.9%

19.0%

79.8%

Hippocampal DMRs

CpG Island

Shores

Other

Loss of DNAm 72%

Gain of DNAm 28%

BLOOD

Loss of DNAm 69%

Gain of DNAm 31%

HIPPOCAMPUS

Ratio of directions of DNA methylation (DNAm) change

How do these numbers compare to the number of genes

that are up- or down-regulated?

Methylation values for each CpG in Vehicle and CORT treated blood DNA

0

200

400

600

800

1000

1200

0 0.1 0.5 1 2 5 10 15 20 25 50 50+

Nu

mb

er o

f D

MR

s

Distance between Common DMRs (X1,000 Basepairs)

Absolute Distance by Kilobases

0

200

400

600

800

1000

0 10 20 30 40 50 60 70 80 90 100 100+

Nu

mb

er o

f D

MR

s

Distance Between Common DMRs Normalized by Gene Length (%)

Distance Normalized by Gene Length

Rat Methyl-Seq

• Platform Design

• Validation

• Implementation on a rat model of stress

Source Site Classification Number of

Targets

Total Bases Covered

(bp)

rat.Nov.2004.m4.refGene Promoters (-/+1kb) 17,620 17,620,000

rat.Nov.2004.m4.cpgIslandGgfAndyM

asked CpG Islands 88,554 42,699,212

rat.Nov.2004.m4.cpgIslandGgfAndyM

asked

Shores (CpG Islands -

/+1kb) 88,554 177,108,000

Rat CHARM GC-rich sequences 32,889 79,820,886

PubMed GR-Targets 1,183 274,007

Total 228,800 317,522,105

Total non-redundant bases covered (after merging overlapping regions from above and

trimming i.e. selecting 500 base pairs region for every 1000 base pairs skipped):

111,038,241 bp; ~2.3 million on-target CpGs; and average region size = 594 bp.

Genomic regions targeted by the SureSelect Rat Methyl-Seq Platform

0

10

20

30

40

50

60

70

80

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6

Cd52: Blood vs. Liver

Blood

Liver

0

10

20

30

40

50

60

70

80

90

100

Shank2: Hippo vs. Liver

Hippo

Liver

30

40

50

60

70

80

90

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7

Cit: Hippo vs. Hypo

Hippo

Hypo

0

20

40

60

80

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7

Grap2: Hippo vs. Blood

Hippo

Blood

Tissue-specific DMRs

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0

20

40

60

80

100

CpG-1 CpG-2 CpG-3 CpG-4

Chr X: Bcor

Hippo Male

Hippo Female

0

20

40

60

80

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6

Chr X: Tsix

Male

Female

0

20

40

60

80

100

Chr X: Xist

0

20

40

60

80

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5

Chr. X: Pgk1

X-chromosomal DMRs

** **

**

** ** *

* **

** **

**

**

**

** **

**

**

** ** **

** **

** ** **

**

0

10

20

30

40

50

60

70

80

90

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6

Chr 13: Lrrn2

Hippo Male

Hippo Female

Autosomal DMRs M

eth

yla

tion

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0.5

0.8 ●

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chr13: 45,731,124 − 45,731,287 (width = 164, extended = 1,000)

**

**

* *

Me

thyla

tion

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0.5

0.8

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chr18: 63,921,357 − 63,921,561 (width = 205, extended = 1,000)

0

20

40

60

80

100

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7 CpG-8 CpG-9

Chr 18: Tubb6

Hippo Male

Hippo Female

* *** ***

** ** ***

*

Chronic variable stress in the rat: stress hormone levels

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25

Pla

sm

a C

OR

T u

g/m

L

Time in Days

Plasma Corticosterone Levels During CVS

STRESS CONTROL

120

160

200

240

280

320

0 50 100 150 200

An

xie

ty-l

ike b

eh

avio

r

Plasma CORT ug/mL

R2=0.17

P=0.077

Chronic variable stress in the rat: behavior

chr start end gene

symbol

distance from

gene areaStat

Mean

Diff stress control direction

chr20 1,644,246 1,644,390 RT1-M4 in_gene 93.03 0.22 0.33 0.11 Gain

chr5 160,361,352 160,361,564 LOC690911 in_gene -70.75 -0.19 0.72 0.91 Loss

chr3 61,138,281 61,138,330 RGD1564319 265569 61.79 0.21 0.94 0.72 Gain

chr2 143,064,811 143,065,010 Ufm1 8569 -59.48 -0.11 0.13 0.24 Loss

chr7 30,764,111 30,764,284 Ntn4 in_gene 57.04 0.21 0.94 0.73 Gain

chr17 12,469,112 12,469,218 Idnk 41996 -50.91 -0.13 0.74 0.88 Loss

chr7 47,101,725 47,101,930 Pawr in_gene -50.54 -0.12 0.64 0.76 Loss

chr5 76,111,248 76,111,822 Txndc8 151703 -50.38 -0.11 0.85 0.96 Loss

chr11 80,640,132 80,640,356 Dgkg in_gene -50.07 -0.16 0.73 0.89 Loss

chr8 71,759,248 71,759,411 Mir190 210226 -47.84 -0.17 0.58 0.75 Loss

Chronic variable stress in the rat: List of DMRs

Gene Networks and Pathways

Me

thyla

tion

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0.5

0.8

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chr20: 1,644,246 − 1,644,390 (width = 145, extended = 1,000)

a)

0

10

20

30

40

50

60

CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7 CpG-8 CpG-9 CpG-10 CpG-11 CpG-12

% D

NA

Meth

yla

tio

n

RT1M4

Control

Stress

b)

* *

*

*

*

*

* *

*

*

p<0.05

Result: Validation of a candidate DMR in the rat

R2=0.54

P=0.001

20

30

40

50

60

0 20 40 60 80 100 120 140 160 180

% D

NA

Meth

yla

tio

n

Plasma CORT Levels ug/mL

RT1M4: DNA Methylation vs. Plasma CORT

Result: Identification of a candidate stress DMR in the rat

Human 30-day Stress Study

Emotional stress high-low: DMR: chr15: 27138591-27140153 (GABRA5, GABRB3)

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

% D

NA

Meth

yla

tion

CpG Position

Emotional Stress Levels and DNA Methylation of GABRA5

Low

High*

**

*

* * **

Current and Future Directions using the Methyl-Seq platform

• Rat stress brain methylation

• Human Methyl-Seq correlation to 30-day saliva cortisol

• Human postmortem bipolar brain study (custom-made Methyl-Seq platform)

Acknowledgements

Mood Disorders Group

Tim Moran

Kellie Tamashiro

Peter Zandi

Fayaz Seifuddin

Olivia Cox

Wand Lab

Gary Wand

Xiaoju Yang

Agilent

Jonathan Levine

Josh Wang

Brigette Kipphut

Mary Napier

Support NARSAD NIMH Rales Foundation